5 citations · 6 across the 8 of their papers we have counts for
8 papers
StartupBench: Benchmarking General-Purpose Agents on Market-Validated End-to-End Workflows
Liya Zhu, Xin Ma, Tao Liu +35
Recent advances in Large Language Models(LLMs) and agents have substantially improved the ability of AI systems to execute complex tasks. Yet existing benchmarks largely rely on re…
Agent-World: Scaling Real-World Environment Synthesis for Evolving General Agent Intelligence
Guanting Dong, Junting Lu, Junjie Huang +17
Large language models are increasingly expected to serve as general-purpose agents that interact with external, stateful tool environments. The Model Context Protocol (MCP) and bro…
MME-CC: A Challenging Multi-Modal Evaluation Benchmark of Cognitive Capacity
Kaiyuan Zhang, Chenghao Yang, Zhoufutu Wen +19
As reasoning models scale rapidly, the essential role of multimodality in human cognition has come into sharp relief, driving a growing need to probe vision-centric cognitive behav…
Game-TARS: Pretrained Foundation Models for Scalable Generalist Multimodal Game Agents
Zihao Wang, Xujing Li, Yining Ye +24
We present Game-TARS, a generalist game agent trained with a unified, scalable action space anchored to human-aligned native keyboard-mouse inputs. Unlike API- or GUI-based approac…
UI-TARS-2 Technical Report: Advancing GUI Agent with Multi-Turn Reinforcement Learning
Haoming Wang, Haoyang Zou, Huatong Song +109
The development of autonomous agents for graphical user interfaces (GUIs) presents major challenges in artificial intelligence. While recent advances in native agent models have sh…
Seed1.5-VL Technical Report
Dong Guo, Faming Wu, Feida Zhu +194
We present Seed1.5-VL, a vision-language foundation model designed to advance general-purpose multimodal understanding and reasoning. Seed1.5-VL is composed with a 532M-parameter v…
MARS-Bench: A Multi-turn Athletic Real-world Scenario Benchmark for Dialogue Evaluation
Chenghao Yang, Yinbo Luo, Zhoufutu Wen +8
Large Language Models (\textbf{LLMs}), e.g. ChatGPT, have been widely adopted in real-world dialogue applications. However, LLMs' robustness, especially in handling long complex di…
UI-TARS: Pioneering Automated GUI Interaction with Native Agents
Yujia Qin, Yining Ye, Junjie Fang +32
This paper introduces UI-TARS, a native GUI agent model that solely perceives the screenshots as input and performs human-like interactions (e.g., keyboard and mouse operations). U…